Hsiao-Wei Hu and Chia-Wei Huang: Constructing a Post Influence-Predicting Model in Social Networking Service Based on NGD and fuzzy C-Means Algorithm, The third international conference on Social Informatics, Harvard University, Cambridge, MA 02138, USA, December 14-16, 2014. NSC-101-MY3.

Hsiao-Wei Hu and Chia-Wei Huang: Constructing a post influence-predicting model in social networking service based on LDA and fuzzy C-Means algorithm, 2014 International Research Conference on Engineering and Technology, Bali, Indonesia, June 27-29, 2014. NSC-101-MY2.

Hsiao-Wei Hu and Kuan-Jung Lien: An Approach Of Frequent Pattern Mining In Cloud-Based Environment, 2013 International Conference on Business and Information, Bali, Indonesia, July 08, 2013. NSC-101-MY1

Hu Hsiao-Wei and Shao-Yu Lee: Study on Influence Diffusion in Social Network, International conference on Data Mining and Its Applications(ICDMA'13), Dubai，United Arab Emirates, March 2013. NSC-101-MY1

Hsiao-Wei Hu: A Novel Decision Tree Classifier for Cloud Service Application with Location Aware System, BAI 2012 International Conference on Business and Information, Japan, July 03, 2012. NSC-100

My current research focuses on association rule mining and classification technique aimed to extract implicit, previously unknown, and potentially useful information from large databases.

Association rule mining
I proposed a new approach to performing market basket analysis in a multiple-store and multiple-period environment. Using the approach, a decision maker could analyze purchasing patterns at very detailed concept levels of time and place, such as a combination of days and stores, at more general levels, such as a combination of quarters and states, and combinations of detailed levels of one with general level of the other, such as a combination of days and regions. In addition to this flexibility, the association rules are well organized, because they were generated according to the contexts derived from the time and place hierarchies. A numerical evaluation shows that the algorithm is efficient in running time and may generate more specific and richer information than the store-chain rules and the traditional rules.
This research has been published in Decision Support System in 2008. Title: “Context-based market basket analysis in a multiple-store environment.”

Classification
Since in most studies, Decision Tree (DT) construction algorithms usually assumed that the class label was a categorical variable or a Boolean variable, i.e., the label must be in a small discrete set of options. In other words, the algorithms operate under the assumption that the class labels are flat. Unfortunately, in real world application, there have more complex classification scenarios, where the class labels to be predicted are continuous variable, hierarchically related or both. For example, the sales volumes of the products and the performance score of a student.
Whereas this limitation, I intended to design a complete, robust, and valuable classification method to complement the scarcity of existing algorithms. This project aimed to (1) propose a novel classification algorithm for learning DT classifier by taking into account the distribution of class labels in the hierarchical tree; (2) design a new DT induction algorithm that can dynamically discretize continuous class labels within individual nodes. In all the above-mentioned items, one must find the best tradeoff between precision and accuracy. This project took 3 years to complete the above-mentioned items.
In the first year, the basic model for conducting DT with continuous class labels has been built up and published in IEEE Transactions on Knowledge and Data Engineering in 2009. Title: “A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label.”
In the second year, a hierarchical class label has been considered in the proposed model to maximize both predictive accuracy and predictive precision. This research has been published in Expert Systems with Applications in 2009. Title: “Constructing a decision tree from data with hierarchical class labels.”
In the third year, the model was modified to suit the situation where the predicted data of DT is continuous variable and could be naturally organized as a hierarchy tree. This research is now under second reviewing process of the Journal of IEEE SMC-B.

Teaching Philosophy
My teaching philosophy acknowledges that we always need to adjust our teaching strategies to serve different audiences. For example, in a course, some students focus on the materials only; others want to learn the materials to support other personal pursuit (e.g. Marketing etc.); and still others are interested in enhancing professional development. When teaching such a diverse class, I believe that it is vital to continually monitor the progress and motivate students – this is especially important with technically dense or mathematical topics that some students may find intimidating. I will try to emphasize how a deeper understanding may ultimately help the students in many ways.

Curricular Plans
I strive to inspire passion through teaching by connecting the material both to cutting-edge research and to the “real world”.
Service Science, in my view, represents the next “big idea” that service (operations) management scholars need to tackle (Chase and Apte 2007). It also means that students must be conversant with a broader range of service system technologies earlier in their careers, essentially gaining breadth earlier and with fewer courses. For example, whereas relational database technology previously was a specialty area in CS, today all software/systems students must be familiar with basic concepts and tools in this area.
Therefore, I plan to develop two courses which are (1) Database management system and (2) Data Mining. These courses are designed towards Ph.D. students and Master’s students interested in doing research. The courses focuses on principles and techniques for constructing data management, storage, integration systems and data mining tools. I will cover traditional database techniques, such as concurrency control, indexing, and recovery, as well as more modern topics such as XML query processing, data integration, peer-to-peer computing, and search engines. Since the role of these courses are primarily to train Ph.D. students, I will emphasize both writing and system-building skills in this course: the students must turn in frequent paper summaries, as well as a project proposal.
In addition, there has been incredible growth and innovation in the field of Internet-centric and Web-centric technologies, sites, and services that people use every day. Virtually everyone interacts with Google services on a daily basis; families keep in touch using photo sharing sites like Flickr and videoconferencing applications like Skype; social networking sites such as Facebook have taken the world by storm; YouTube and iTunes are revolutionizing the audio and video realm. Text messaging, iPhones, and Blackberries have changed communication forever. I believe that our teaching of Service Science needs to be updated in accordance with this network-centric, communication-centric, data-centric world.
As a first step in this direction, I plan to develop a course called “Internet and Web Service Systems”. this course is intended to help launch Master students towards careers in Web Service-related areas and to inspire Ph.D. students come up with new research ideas through the course. This course combines topics from distributed systems, information retrieval, Web services, Data mining, and data interchange, with a focus on issues related to scalability, reliability, consistency, and distribution.

Summary
Academia provides an opportunity to combine research and teaching together. I believe the role of a professor is to be an educator informed by the “real world” and its needs, good pedagogical techniques, and the best ideas from research. I will strive to do this, and to encourage and engage students both in the classroom and in the research. In addition, I will take my teaching responsibilities to heart, both by considering how to take advantage of the teaching and research interaction, and also by continually re-evaluating how trends in industry should reflect upon my teaching.
Academia is fun. We get the best students, and mentoring them through research, teaching, advising, and general life skills. Having said that, this is not only rewarding but also the surest path to lasting impact for students.

Reference
Chase, R.B., Apte, U.M. (2007). A history of research in service operations: what’s the big idea? Journal of Operations Management 25: 375-386.